Distinctive features of persuasion and deliberation dialogues
Bibliographic record
Abstract
The distinction between action persuasion dialogues and deliberation dialogues is not always obvious at first sight. In this paper, we provide a characterisation of both types of dialogues that draws out the distinctive features of each. It is important to recognise the distinctions since participants in both types of dialogues will have different aims, which in turn affects whether a successful outcome can be reached. Such dialogues are typically conducted by exchanging arguments for and against certain options. The moves of the dialogue are designed to facilitate such exchanges. In particular, we show how the pre- and post-conditions for the use of particular moves in the dialogues are very different depending upon whether they are used as part of a persuasion over action or a deliberation dialogue. We draw out the distinctions with reference to a running example that we also present as a logic program in order to give a clear characterisation of the two types of dialogues, which is intended to enable them to be used more effectively within systems requiring automated communication.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".